Claims overpayments are more than a nuisance; they are an epidemic. Between $68 and $226 billion is lost annually to Fraud, Waste and Abuse (FWA), according to the National Health Care Anti-Fraud Association (NHCAA). As much as 10 percent of healthcare spending is attributed to abuse alone⁽i⁾, with the federal government losing more than $70 billion to improper Medicare and Medicaid payments in 2010.(ii)

This fact was not overlooked in the Patient Protection and Affordable Care Act (PPACA), which included provisions that will enhance data-sharing efforts among payers and impose stricter penalties for those who abuse the system. And while the new rules written into PPACA are largely devoted to eradicating FWA in Medicare and Medicaid, commercial health insurers stand to benefit from Health and Human Services’ (HHS) increased screening and data-sharing efforts designed to prevent overpayments—whether the result of abuse or simply a billing error—rather than trying recoup them after the claim has been settled.

Paying on an inaccurate claim and then attempting to recover the money is clearly inefficient. Follow-up activities, including letters, phone calls and even legal action, if necessary, can significantly erode a claim’s original value. This “pay-and-chase” post-payment recovery model, however, will remain part of the landscape due in part to an HHS ruling that only fraud program recovery costs can apply toward  medical loss ratio (MLR) rates—a byproduct of PPACA that has increased certain regulations on health insurers.(iii) Moreover, post-payment recoupment may be necessary in complex cases when payers must honor prompt-pay regulations while collecting information that may reveal an overpayment. And in many cases, an effective audit and recovery program can offer insights into overpayments with root cause analysis and process improvement recommendations to help prevent claims errors from reoccurring in the future.

At the same time, most health plans are continuing to invest in efforts to prevent overpayments, knowing the money they will save by identifying erroneous claims early strengthens their bottom line.

Traditional Rules-Based Approaches Lose Favor

For health plans already feeling the financial pinch of a constrained economy, rising medical costs and increased competition, gaining control of overpayments is of paramount importance. Many insurers have long utilized technology to identify these claims, with mixed results. Traditional rules-based systems apply “if-then” scenarios to an analysis of medical claims in an attempt to recognize abnormalities or suspicious patterns.

While these efforts are vital to an overpayment prevention strategy, many insurers have made significant technology investments only to find the solutions have not produced the returns promised or expected. At the heart of the problem is the health plans’ approach to detection: applying payer-centric models to a provider-centric problem. For the most part, payers are utilizing their own data sets to determine aberrancy, meaning they can analyze only those claims submitted to their company.

More value is offered when health plans have the ability to trace a doctor’s billing patterns across multiple payers, including Medicare and Medicaid. When this rich data set is combined with technology that applies additional intelligence to overpayment detection, health plans have an opportunity to achieve remarkable results.

Sophisticated Analytics Give Insurers a Leg Up

To decrease overpayments health plans are supplementing post-payment collection efforts with effective prospective detection solutions. Some are implementing analytical technologies to identify possible claim discrepancies at the time a claim is adjudicated. These tools combine predictive, data-driven, integrated code edits and clinical aberrancy rules to identify claim outliers. Unlike rules-based systems, data-driven analytical solutions examine hundreds of variables, and can detect previously unknown and emerging patterns that rules-based analytics may not recognize.

Intuitive solutions are capable of ranking each claim to determine the measure of risk it represents, helping payers quickly and efficiently determine the best course of action. They are also able to combine data from multiple sources to provide a more complete view of potential FWA by providing a conduit between clinical edits and predictive analytics. Examining expanded data sources in addition to those claims submitted to their company also extends a health plan’s ability to establish broader aberrancy rules.

An additional layer that can deliver savings to a multi-faceted payment integrity program  is to reduce billing overpayments that result from improper coding. This can be achieved by supplementing analytics with clinical code edit technologies backed by nationally recognized coding guidelines as they are designed to find coding errors, unbundled treatments, unusual and inconsistent treatment patterns, and inappropriate diagnoses.

Multidimensional Strategies Yield Results

Analytics are certainly a pillar in an effective, comprehensive payment integrity program. And another important element would be predictive-modeling scoring solutions that can examine hundreds of variables in innumerable combinations simultaneously. It’s not a catchall, however. A full-featured strategy that includes claims scoring predictive analytics and rules-based detection technologies is vital for today’s health plans. Not only does it provide concrete return on investment (ROI) by assisting in the recovery or prevention of overpayments, it may also deliver a sentinel effect—discouraging would-be FWA offenders from making improper submissions to health plans that utilize intelligent solutions.

Furthermore, solutions that combine data from multiple sources provide a more complete view by providing a conduit between clinical edits and predictive analytics to determine clinical aberrancy rules. Unlike traditional clinical edits or fraud-based rules based on a single payer’s data, aberrancy rules look across multiple data variables, timeframes and data from many different payers to identify claim anomalies, which trigger an alert prompting additional review of the claim.

To realize the full value of a payment integrity solution, payers must have an adequate and trained staff to manage the solution. Progressive health plans are combining technology with expertise offered by knowledgeable investigators with backgrounds in law enforcement, criminal justice, private investigation, claims investigation, statistics and analytics. These individuals are tasked with reviewing and analyzing historical claims data, medical records, suspect provider databases and high-risk identification lists while also conducting patient and provider interviews.

As it begins and then continues to detect overpayments, a payer can employ additional resources to work toward achieving greater ROI. The technology a payer chooses helps determine what resources it needs to make the program successful. Since the introduction of a payment integrity solution affects several groups within an organization, it is essential that executive management gain the support and involvement of the special investigative unit (SIU), claims, provider relations, finance, legal and administrative departments to successfully move to a prepayment detection model. Workflows and processes will likely change, but subsequent benefits should become apparent.

Conclusion

Ongoing industry challenges, including claim processing complexities, healthcare reform and competition often force payers to impose increased premiums and coverage limitations for patients. Payers that once presumed they could not afford to invest in fraud prevention now realize that market forces and internal financial pressures make critical the need to identify and prevent unnecessary claims payments.

Detecting and preventing erroneous claims prepayment is a strategic way for payers to reduce liabilities and improve overall financial health. Health insurers, however, rarely have the depth of information gleaned from analyzing multiple payers’ claims data. Traditionally employed post-payment recovery approaches to overpayment management can be augmented with pre-payment solutions to achieve meaningful payment integrity.

Effective prepayment solutions that draw upon multi-payer data and sophisticated analytics are emerging to fill the void. Further, by leveraging the power of predictive analytics to continuously identify the many avenues for overpayment, including FWA, and by prioritizing claims that are likely to be overpaid, payers can take tremendous steps toward effectively reducing overpayments and maximizing revenue.
 

i FBI Financial Crimes 2008, http://www.fbi.gov/stats-services/publications/fcs_report2008
ii "Medicare and Medicaid Fraud, Waste and Abuse,” Senate Subcommittee on Federal Financial Management, Government Accountability Office.
iii Centers for Medicare and Medicaid Services (CMS, Medical Loss Ratio: Getting Your Money’s Worth on Health Insurance (Final Rile Fact Sheet), http://cciio.cms.gov/resources/factsheets/mlrfinalrule.html).